Tools

"... The cross-entropy (CE) method is a new generic approach to combinatorial and multi-extremal optimization and rare event simulation. The purpose of this tutorial is to give a gentle introduction to the CE method. We present the CE methodology, the basic algorithm and its modifications, and discuss a ..."

The cross-entropy (CE) method is a new generic approach to combinatorial and multi-extremal optimization and rare event simulation. The purpose of this tutorial is to give a gentle introduction to the CE method. We present the CE methodology, the basic algorithm and its modifications, and discuss

"... The cross-entropy method is a versatile heuristic tool for solving difficult estimation and optimization problems, based on Kullback–Leibler (or cross-entropy) minimization. As an optimization method it unifies many existing populationbased optimization heuristics. In this chapter we show how the cr ..."

The cross-entropy method is a versatile heuristic tool for solving difficult estimation and optimization problems, based on Kullback–Leibler (or cross-entropy) minimization. As an optimization method it unifies many existing populationbased optimization heuristics. In this chapter we show how

"... This paper proposes a novel approach to SAT solving by using the cross-entropy method for optimization. It introduces an extension of the Boolean satisfiability setting to a multi-valued framework, where a probability space is induced over the set of all possible as-signments. For a given formula, a ..."

This paper proposes a novel approach to SAT solving by using the cross-entropy method for optimization. It introduces an extension of the Boolean satisfiability setting to a multi-valued framework, where a probability space is induced over the set of all possible as-signments. For a given formula

"... The cross-entropy and minimum cross-entropy methods are well-known Monte Carlo simulation techniques for rare-event probability estimation and optimization. In this paper we investigate how these methods can be extended to provide a general non-parametric cross-entropy framework based on φ-divergenc ..."

The cross-entropy and minimum cross-entropy methods are well-known Monte Carlo simulation techniques for rare-event probability estimation and optimization. In this paper we investigate how these methods can be extended to provide a general non-parametric cross-entropy framework based on φ

"... This chapter describes how difficult statistical estimation problems can often be solved efficiently by means of the cross-entropy (CE) method. The CE method can be viewed as an adaptive importance sampling procedure that uses the cross-entropy or Kullback–Leibler divergence as a measure of closenes ..."

This chapter describes how difficult statistical estimation problems can often be solved efficiently by means of the cross-entropy (CE) method. The CE method can be viewed as an adaptive importance sampling procedure that uses the cross-entropy or Kullback–Leibler divergence as a measure

"... Abstract—In simulation-based verification, we check the cor-rectness of a given program by executing it on some input vectors. Even for medium-size programs, exhaustive testing is impossible. Thus, many errors are left undetected. The problem of increasing the exhaustiveness of testing and decreasin ..."

and decreasing the number of undetected errors is the main problem of software testing. In this paper, we present a novel approach to software testing, which allows us to dramatically raise the probability of catching rare errors in large programs. Our approach is based on the cross-entropy method. We define a

"... We consider support vector machines for binary classification. As opposed to most approaches we use the number of support vectors (the “L0 norm”) as a regularizing term instead of the L1 or L2 norms. In order to solve the optimization problem we use the cross entropy method to search over the possib ..."

We consider support vector machines for binary classification. As opposed to most approaches we use the number of support vectors (the “L0 norm”) as a regularizing term instead of the L1 or L2 norms. In order to solve the optimization problem we use the crossentropy method to search over

"... The Cross-Entropy (CE) method is a modern and effective optimization method well suited to parallel implementations. There is a vast array of problems today, some of which are highly complex and can take weeks or even longer to solve using current optimization techniques. This paper presents a gener ..."

The Cross-Entropy (CE) method is a modern and effective optimization method well suited to parallel implementations. There is a vast array of problems today, some of which are highly complex and can take weeks or even longer to solve using current optimization techniques. This paper presents a

by
Patrick Juola
- Proceedings of New Methods in Language Processing 3, 1998

"... The idea of "familial relationships" among languages is well-established and accepted, although some controversies persist in a few specific instances. By painstakingly recording and identifying regularities and similarities and comparing these to the historical record, linguists hav ..."

that Kullback-Leibler divergence (or cross-entropy) can be meaningfully measured from small samples, in some cases as small as only 20 or so words. Using these techniques, we define and measure a distance function between translations of a small corpus (c. 70 words/sample) covering much of the accepted